According to data from Apprenticeship Management System Aptem, enrolment in apprenticeships following the Artificial intelligence (AI) data specialist standard grew 42% in the last year. There’s a growing interest in alternative learning paths for AI and data science beyond traditional degrees.

As an aspiring data scientist, it's crucial that you weigh the pros and cons of apprenticeships and degrees to decide which route is best for you.

Keep reading to unravel what sets data science degrees apart from Level 7 AI apprenticeships. 

What is a data science degree?

Data science degrees are specialised academic programmes focussed on educating students in the fields of data analysis, machine learning, statistics, and big data technologies. Every genuine UK degree course is accredited by a university or an officially recognised body that grants the final qualification.

These degrees aim to equip students with the skills and knowledge required to interpret complex data and drive decision-making processes in various industries. 

A typical data science degree offers a thorough exploration that begins with basics in data science concepts, tools, and applications. It includes essential topics such as statistics, probability, and programming languages like Python and R, progressing to data wrangling and analysis techniques. 

Students engage in key modules like machine learning, focusing on predictive modelling, and big data technologies.

The curriculum is rounded out with data visualisation, ethical considerations in data science, and the possibility of work placements for real-world skill application.

Benefits of pursuing a data science degree

Opting for a degree over an apprenticeship offers certain benefits:

1. Comprehensive theoretical knowledge: Data science degrees provide a thorough understanding of fundamental theories and principles. This deep academic grounding in subjects like statistics, algorithms, and machine learning theory is essential for complex problem-solving and innovation in the field.
2. Structured and diverse curriculum: University programmes offer a structured approach to learning, covering a wide range of topics. This diverse curriculum ensures a well-rounded education, equipping students with a broad skill set applicable across various industries.
3. Recognition and credibility: Though skills-based hiring is certainly on the rise, a degree from a reputable institution will not be overlooked. It often opens doors to higher-level positions and can be particularly advantageous for those seeking careers in academia or research-intensive roles.
4. Flexibility for further education: A degree lays a solid foundation for further education, such as pursuing a master's (equivalent to the L7 AI apprenticeship) or even a PhD.

A data science degree provides a comprehensive, theory-based education with diverse learning opportunities. This pathway sets a strong foundation from the get go for a potentially versatile career in data science.

Challenges of data science degrees

While a degree offers numerous benefits, it also presents specific challenges, especially when compared to an apprenticeship:

• Intensive academic rigour: Data science degrees demand a high level of academic commitment. The theoretical and mathematical aspects of the curriculum can be challenging, especially for those less inclined towards theoretical learning, compared to the hands-on approach of apprenticeships.
• Time commitment: Pursuing a full-time degree requires a significant time investment. This is more time-consuming than apprenticeships, which allow for learning while working.
  
• Financial investment: University education often comes with substantial tuition fees and associated costs.
  
  In 2023 university drop out rates reached an all-time high. The Student Loans Company (SLC) reported a 28% rise over five years in students who signed up for a loan before dropping out of a course. With 8% of students citing “financial distress” as their primary reason for dropping out.
  
• Limited practical experience: While degrees provide a broad theoretical foundation, they typically offer less practical experience than equivalent apprenticeships, which can be a drawback in skill application and job readiness.
• Adaptability to industry changes: The rapid evolution of data science can sometimes outpace academic curricula, potentially leading to a gap between university learning and current industry practices.

Cost and duration

A typical degree in data science spans 3 to 4 years. The 4th year is dedicated to working towards the master’s qualification after the initial bachelor’s degree.

But tuition fees for degrees and additional master qualifications can be high. Tuition fees for UK and EU students at public universities range from approximately £9,000 to £9,250 per year. In total, a UK or EU based student can end up spending anywhere up to £27,750 just in tuition fees.

Besides tuition, students should also consider additional costs like accommodation, books, and living expenses, which can add a substantial amount to the overall expense of pursuing a degree.

What is an L7 AI apprenticeship?

A Level 7 AI Apprenticeship is an advanced training programme designed to equip working professionals with cutting-edge skills in AI and data science. This apprenticeship blends industry-relevant knowledge with practical applications and is led by expert faculty.

Let's look at the modules within the Cambridge Spark L7 AI Apprenticeship curriculum and what each module involves:

1. The data science toolbox: Introduces learners to common, industry-standard tools for data analysis and data science in Python​​.
2. Introduction to machine learning: Builds familiarity with advanced concepts and tools for various machine learning models and techniques​​.
3. Product management for AI: Develops a customer-centric product mindset, emphasising understanding users to create effective solutions​​.
4. Supervised classification: Covers an array of supervised learning models and techniques, focusing on model suitability and performance enhancement​​.
5. Ensemble methods: Introduces key concepts in ensemble methods, including bagging, random forest, boosting, gradient boosting, and advanced techniques​​.
6. Pragmatic model evaluation: Teaches a suite of evaluative techniques for different types of data science problems​​.
7. Unsupervised learning: Covers unsupervised learning models and techniques to uncover latent structures in data​​.
8. The AI landscape: Explores ethical considerations and data privacy regulations impacting AI development and deployment​​.
9. Time series analysis: Builds expertise in tools and techniques for working with time series data​​.
10. Practical hackathon: Involves collaborative team work on real-world projects to apply skills in a simulated environment​​.
11. Neural networks and deep learning: Teaches the construction and training of neural networks, including CNNs, RNNs, GANs, and Graph Neural Networks​​.
12. Model explainability and interpretability: Focuses on approaches and techniques for interpreting and explaining machine learning models and neural networks​.

This comprehensive curriculum is designed to ensure you’re well-prepared to tackle real-world challenges, specific to your role and industry.

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Benefits of pursuing an AI apprenticeship

A significant advantage of apprenticeships is that they are fully funded by the UK Apprenticeship Levy. This funding arrangement means that you don’t incur any personal, financial cost for participating in a programme. This is a major advantage over bearing the costs of pursuing a degree.

Choosing an apprenticeship over a traditional degree presents several other unique advantages too:

• Practical experience: Apprentices gain hands-on experience working on real-world projects in their own role and industries. This experience is invaluable for understanding practical applications of AI and data science and contrasts with the more theory-focused approach of a degree.

“I had researched other providers of apprenticeships. But the thing that stood out for me with Cambridge Spark was the focus on business. You can apply data science in many different areas. But with Cambridge Spark, they will help you directly apply your learning to solve real problems in your business. And for me that was like... Wow!” 

- Elle Neal, Level 7 AI apprentice and Data Scientist, BPA Quality

• Industry-relevant skills: Apprenticeship providers tend to be more agile than universities. This allows them to continually update curriculum  to meet current industry needs, ensuring apprentices learn skills and software that are immediately applicable and in high demand.
• 1:1 Mentorship: Apprentices often benefit from direct guidance from their own dedicated coaches and mentors throughout their entire apprenticeship journey.
   
  Some providers, including Cambridge Spark, are distinct in  separating coach and mentor roles. Mentors focus mainly on enhancing learners' technical data skills, while coaches help learners manage their time and overall career goals. 
  
  This approach differs from other providers’ where a single person performs both roles of a coach and a mentor.
• Flexibility: Data science apprenticeships are often delivered online, which gives learners some flexibility on how and where they complete their apprenticeship.
  
  In Cambridge Spark’s case, learners can access course materials 24/7 allowing for round the clock feedback on their work from our online learning platform EDUKATE.AI. 
  
• Networking: Learners have the opportunity to build a network and work alongside their peers. Cambridge Spark learners even get access to exclusive events and insight sessions hosted by data experts. 
  
  They also have the opportunity to attend in person events which is crucial for building relationships with other professionals in the data community. And these relationships often help with future career development.

Challenges of AI apprenticeships

The main challenge encountered by learners in an AI apprenticeship is the effective management of their time. Balancing work and life obligations with academic commitments can seem daunting at first. 

Support from a coach and mentor throughout the apprenticeship can help you mitigate this challenge and effectively manage workloads.

Additionally, support from the learner's line manager can also ensure a smoother and more manageable apprenticeship experience.

Industry demand for skills vs. academic credentials 

For years, degrees were the pinnacle in employer considerations when scouting new talent. But there has been a notable shift towards “skills-based hiring”. In 2023, as many as 58% of companies expressed plans to ramp up their focus on skills-based hiring. This is a total increase of 13% compared to 2022.

This increase could be due to a number of reasons. Companies that have used skills-based hiring as part of their recruitment process have reported seeing benefits such as improved retention rates, better quality of candidates and reduced hiring costs.

Apprenticeships, like the Level 7 AI apprenticeship, align perfectly with the skills-based hiring approach, as they emphasise hands-on, practical training. They equip learners with specific, industry-relevant skills that meet the immediate needs of employers.

Conclusion

Degrees and apprenticeships each offer unique pathways into the rapidly evolving field of data science and AI. While degrees provide a strong theoretical foundation and academic credibility, apprenticeships offer practical experience, industry-relevant skills, and the financial advantage of public funding. 

If you're considering a path in this dynamic field, learn more about the Level 7 AI apprenticeship or get in touch with us by filling out the form below.